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로컬 실행이나 지속적 통합에서 발견한 Python 타입 검사 오류를 분류하고, 타입 좁히기와 주석 수정부터 재검증까지 진행합니다. 새로운 타입 검사 범위 추가도 다룹니다.

• 지속적 통합의 타입 검사 실패 수정
• None이 포함된 타입 오류 수정
• 타입 검사 대상 디렉터리 추가

Review a diffusers contribution before opening a pull request. Check the full change against project rules, trace potentially unused code, and receive a severity-based report separating required fixes from points to discuss with reviewers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking a diffusers contribution before opening a pull request
• Investigating potentially unused code in a change
• Reviewing fixes again and preparing notes for reviewers

Run diffusers generation pipelines from a terminal and save their outputs. Inspect accepted inputs without downloading model weights, apply LoRA adapters during generation, run remotely through HF Jobs, and inspect software and GPU environment details.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running one-off generation or batch jobs from a terminal
• Checking a new model's basic operation
• Inspecting accepted inputs before running a pipeline

Integrate a model or pipeline into diffusers, from file structure and weight conversion to tests and documentation. Verify numerical agreement with the original implementation, with modular pipelines as the preferred starting point for new integrations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a new model and pipeline to diffusers
• Converting a pipeline to modular format
• Converting weights for a new version of a supported model

Package an existing custom diffusers block into a directory ready for Hub upload. Select a class from a Python file and save its source, configuration, and associated files so others can load the block.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a custom block for Hub distribution
• Selecting one block class from a Python file
• Creating the directory layout needed to load a shared block

Check that changes to existing PEFT methods still work with saved training checkpoints. The skill guides compatibility tests, configuration checks, benchmark comparisons, and reporting, including comparisons of model outputs before and after a change.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking compatibility after a forward method rewrite
• Changing state_dict structure while supporting old checkpoints
• Reviewing defaults for new configuration fields

Write and debug code for further training large language models with TRL. Match training methods to dataset formats, configure supervised or preference-based training, and set up reward functions through Python or the command line.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Training a language model on example responses
• Training with preferred and rejected response pairs
• Debugging GRPO reward functions and batch settings

Add or review research paper entries in TRL’s paper index. Organize each paper’s contribution, its connection to the implementation, and a Python configuration example using training settings supported by the paper.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Documenting a newly implemented research method
• Checking paper index updates during PR review
• Recording training settings from a paper

Train sentence-transformers models for search, similarity, and reranking, which reorders search results by relevance. Choose a model type, prepare data, configure training, compare results against a baseline, and publish the trained model to Hugging Face Hub.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fine-tuning an embedding model for domain-specific search
• Training a model to rerank search results
• Training SPLADE or ColBERT retrieval models

Build text, image, and audio features in JavaScript using machine learning models that run in browsers or server runtimes. Covers model selection, execution settings, caching, and memory cleanup without requiring a Python server.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add sentiment analysis to a JavaScript app
• Build image classification in a browser
• Implement audio transcription

Find AI models for a specific task using official Hugging Face benchmark leaderboards. Compare scores, model sizes, and licenses, with memory-based estimates of device suitability and flags for models requiring quantization or API access.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare coding or reasoning models by benchmark scores
• Find model candidates for a laptop's available memory
• Choose models for OCR or speech recognition

Register arXiv papers on Hugging Face Hub, connect them to models and datasets, and manage authorship claims and profile visibility. Create research articles from Markdown templates and convert them into HTML for sharing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Registering an arXiv paper on Hugging Face Hub
• Linking papers to models and datasets
• Managing authorship claims and profile visibility

Check the AWS profile, region, account ID, and caller identity before starting AWS work. Read local configuration and validate credentials, then flag missing settings, expired access, or possible IAM restrictions associated with SSO authentication.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check the AWS account and region before deployment
• Identify the active profile when none was specified
• Detect expired credentials or missing configuration

Prepare an isolated Python environment for SageMaker deployments and AWS automation. The skill selects a supported Python version, installs current AWS packages, and checks versions so installation and execution use the same environment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare a Python environment for SageMaker deployment
• Separate AWS automation dependencies from system Python
• Check installed AWS package versions

Build reusable command-line scripts that fetch and process Hugging Face API data. Combine multiple requests, enrich model information, and produce outputs for further filtering, with built-in help and usage examples for repeated tasks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Automating repeated model and dataset lookups
• Combining trending models with metadata and paper links
• Extracting licenses and tags from model cards

Manage Hugging Face Hub through the hf command line tool. Download and upload models or datasets, organize cloud storage, manage authentication and local caches, run scheduled jobs, and deploy or maintain model inference services.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Download or publish models and datasets
• Sync training checkpoints to cloud storage
• Inspect and clean the local cache

Evaluate Hugging Face Hub models using inspect-ai or lighteval, with local inference or a provider-backed option. Choose an inference engine, start with a small test, and switch engines when model compatibility requires it.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run benchmark evaluations on a local GPU
• Choose an evaluation framework and inference engine
• Check a small sample before a full evaluation

Track machine learning experiments with Trackio by recording training measurements, viewing live dashboards, and flagging diagnostic events. Retrieve metrics and alerts through command-line queries, with JSON output for automated monitoring and experiment review.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording training loss and accuracy
• Flagging loss spikes or stalled training
• Monitoring cloud training through a Space dashboard

Estimate inference memory for models on Hugging Face Hub without downloading their weights. Check GPU capacity for Safetensors or GGUF files, with optional estimates for the KV cache, memory used to retain attention data during generation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check whether a model fits on a GPU
• Compare memory estimates for GGUF quantizations
• Estimate KV cache memory for a chosen context length

Create interactive web demos and chat interfaces in Python with Gradio. Connect functions to text, image, audio, and file controls, arrange screen layouts, and define what happens when users click buttons or change inputs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning a Python function into an interactive demo
• Building a chatbot interface
• Adding image and audio inputs and outputs

Find and run local AI models with llama.cpp. Choose GGUF model files and quantization levels that fit available memory, launch a command line session or local server, and test an OpenAI-compatible API on your computer.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find models to run on a local computer
• Choose quantization for available memory
• Launch and test a local OpenAI-compatible API

Build and publish a browser demo for a supplied LoRA, a model adaptation for image or video tasks. Tailor the Gradio interface to its inputs and follow the model’s recommended settings on Hugging Face Spaces.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Share a trained LoRA through a browser demo
• Build an interface tailored to image editing or video generation
• Apply recommended trigger words and generation settings

Deploy SageMaker endpoints, the services that answer model requests, with automatic capacity scaling, CloudWatch alarms, and resource tags. Supports real-time and asynchronous inference, scaling down to zero instances, deployment checks, and resource cleanup.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deploying a real-time model endpoint
• Scaling a sparse workload down to zero instances
• Running long inference jobs asynchronously

Prepare SageMaker deployments and training by finding existing execution roles, checking trust settings, and reviewing required permissions. Guides role creation only when necessary and identifies permission issues that require an AWS administrator.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check execution roles before deploying a SageMaker endpoint
• Find an existing role when no ARN is provided
• Investigate IAM AccessDenied errors